Nugget AI

Nugget AI | Turn customer interviews into your product roadmap | Product Hunt

Nugget AI

Launching today

Turn customer interviews into your product roadmap

170 followers

Turn customer interviews into your product roadmap

170 followers

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Nugget AI turns customer interviews into product evidence. Record or upload calls → AI extracts pain points and feature requests → synthesis surfaces themes → auto-generated PRDs with real customer quotes → dev-ready handoff to Linear & GitHub. NEW: MCP server. Connect Claude, ChatGPT, Cursor, or Codex — your AI agent can search every interview and draft specs grounded in real evidence. No more copy-pasting. Half the price of Dovetail.

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Hey Product Hunt 👋 I'm Brodie — PM by day, builder by night. Nugget is my answer to a problem I've lived with for 6+ years of doing customer discovery: great interviews generate incredible signal, then it dies in transcripts no one reads. Every PM I know has the same broken workflow: run 5 great interviews → take messy notes → forget half of it → write a PRD from gut feeling → pretend it was data-driven. Engineers then ship from your gut, not your customers. Nugget closes that loop: 🎙️ Real-time transcription (or drop in Zoom, support tickets, Slack threads) 🤖 AI extracts "Nuggets" — pain points, feature requests, sentiment 🧩 Cross-interview synthesis surfaces themes ranked by frequency + severity 📄 Auto-generated PRDs with real customer quotes attached 🔌 NEW: MCP server so Claude, ChatGPT, Cursor, and Codex can query your interviews directly The MCP piece is the one I'm most excited about. Your AI agent stops hallucinating users and starts citing real ones. 🎁 Alpha Day offer: 66% off the first year of Pro for anyone who comments here — DM me your handle. Would love honest feedback — especially from PMs and founders doing discovery. What's broken in your workflow that I haven't solved yet?

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24h ago

Can you walk through how “Smart Prioritization / opportunity scoring” works in practice—what inputs it uses (frequency, severity, segment, recency, goals), how it avoids overweighting loud customers, and what knobs a PM can tune to match their strategy?

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11h ago

Maker

@curiouskitty Good question. The short version: opportunity scoring uses frequency, severity, segment, and recency as inputs — but those are weighted against the goals and strategy you define on the Priorities page. That's the main tuning surface.

This is also how we handle the loud-customer problem. Without strategic context, scoring just reflects volume. Because Nugget anchors to your priorities, a critical pain point from a key segment can outrank a noisy-but-irrelevant request. The PM's judgment about what matters is baked into the system, not overridden by it.

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9h ago

Indie iOS founder here, doing a lot of customer discovery ahead of an App Store launch, so the "interviews die in transcripts no one reads" line hits home. The MCP server is the smart unlock; an agent citing real users instead of hallucinating them is the whole game.

Answering your q: the gap I'd flag is upstream of synthesis: interview quality varies wildly, and a confident-but-shallow interview produces clean-looking Nuggets that are actually just the customer agreeing with my leading questions. Does Nugget do anything to flag low-signal interviews (interviewer talked 70% of the time, all closed questions, sentiment too uniformly positive), or does it treat every transcript as equally trustworthy evidence?

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18h ago

Maker

@ferdi_sigona honestly the exact upstream problem we obsessed over.

The Mom Test is baked into Nugget at two layers: Pre-interview: a Mom Test script generator that takes your hypothesis and produces non-leading, behavior-focused questions (past actions over future intent, specifics over abstractions). It flags leading or solution-seeking phrasing before you ever hit the call.

Post-interview: synthesis scores each transcript for signal quality before nuggets get extracted — interviewer talk ratio, open-vs-closed question mix, sentiment distribution, follow-up depth. Low-signal interviews get flagged so they don’t pollute the evidence base.

Exactly the goal you described: an agent citing real users instead of laundering confirmation bias as insight.

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16h ago

I really like nugget AI Does it integrate with systems like Jira or any type of kanban board?

What type of artifacts does it help the PMs produce?

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15h ago

Maker

@myrto_skourletou Thanks Myrto, really appreciate that!

Jira and Monday integrations are actively in the works, and we already have a limited Linear integration running with a few existing clients. Which one would be most useful for your workflow?

On artifacts: Nugget helps PMs produce PRDs, themes, priorities, and insight reports grounded in customer interviews — all quickly shareable into Slack so the team stays in the loop.

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15h ago

@brodie_sv thank you - i missed the section integrations which is very clear on your home page! love it! good luck!

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15h ago

@brodie_sv i think the tools you integrate are more than enough, but some companies may stick to less and more traditional. I would vote Jira - i think it solely depends on what tools companies use for their PM teams.

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14h ago

I’ve personally been in situations where we did multiple customer calls, captured tons of feedback, and still ended up writing PRDs mostly from memory because nobody had time to go back through long transcripts. A lot of valuable insights just get lost in docs and scattered notes.

Really like how Nugget focuses on connecting customer conversations directly to product decisions instead of letting that context disappear. The MCP integration is especially interesting because AI tools become much more useful when they can reference actual user feedback instead of assumptions.

Congrats on the launch, Brodie!

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15h ago

Maker

@sanjana_yadav2 Thanks Sanjana, this is exactly the pain that pushed us to build Nugget. The PRD-from-memory loop is so common, and it’s wild how much hard-won customer signal just evaporates between the call and the doc.

Two things we built specifically for this: an insight feed that auto-surfaces themes from your interviews and shares them with the team (so the context lives where decisions happen, not buried in transcripts), and structured insight reports that roll up patterns across calls into something you can actually act on. The MCP layer then makes all of that referenceable when you’re drafting in Cursor, Claude, etc.

Really appreciate the kind words, means a lot coming from someone who’s lived the problem.

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15h ago

Love the MCP server → Cursor setup.

Does it auto-pull context from all past interviews, or do you select specific ones for each session?

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15h ago

Maker

@munis_abbas Thanks Munis! Hybrid setup — it auto-pulls interviews relevant to the session’s topic instead of loading everything or making me select manually. Gets me the connective tissue across past convos without blowing up the context window.

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15h ago

hi brodie, the right pain to solve! how do you weight one confident interview against five lukewarm ones? and what stops the AI from inventing patterns from a thin sample? very cool - congrats and good luck!

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12h ago

Maker

@hiyamojo Really good pushback - both of those are live design problems.

Honest answer on weighting: we don't want the AI making that call. It should surface the signal strength and the context (who said it, when, with what level of conviction) and let the PM decide. That judgment call is the job.

On invented patterns: strict attribution is the constraint. No pattern without a source, and thin sample sizes are surfaced explicitly. The goal is for AI to be a rigorous research assistant, not an opinionated analyst.

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12h ago

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